Investigation of charge preparation strategies for controlled premixed charge compression ignition combustion using a variable pressure injection system

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http://jer.sagepub.com/content/11/4/257The online version of this article can be found at:

DOI: 10.1243/14680874JER06409

2010 11: 257International Journal of Engine ResearchS L Kokjohn and R D Reitz

combustion using a variable pressure injection systemInvestigation of charge preparation strategies for controlled premixed charge compression ignition

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Investigation of charge preparation strategies forcontrolled premixed charge compression ignitioncombustion using a variable pressure injection systemS L Kokjohn and R D Reitz*

Department of Mechanical Engineering, The University of Wisconsin–Madison, Madison, Wisconsin, USA

The manuscript was accepted after revision for publication on 12 April 2010.

DOI: 10.1243/14680874JER06409

Abstract: This paper uses a multi-dimensional computational fluid dynamics (CFD) codecoupled with detailed chemistry, the KIVA-CHEMKIN code, to provide guidelines for solvingproblems with premixed combustion strategies, namely, lack of combustion phasing control,excessive pressure rise rate, and spray wall impingement due to early injections. A multipleinjection concept is used to control combustion phasing and reduce the rate of peak pressurerise. To address spray–wall impingement, an adaptive injection strategy (AIS) using two-injection pulses at different injection pressures is employed.

The combustion process considered is at a mid-load operating condition for the light-dutyengine of the present study (nominal indicated mean effective pressure (IMEP) of 5.5 bar andhigh speed, 2000 r/min) and the effects of first and second pulse injection pressure and timing,swirl ratio, and spray targeting are explored. The investigation showed that an optimized low-pressure early cycle injection combined with a high-pressure near top dead centre (TDC)injection allows combustion phasing to be well controlled while achieving premixed com-pression ignition (PCI)-like emissions levels. An improved solution was found with near-zeronitric oxides (NOx) and soot, a net indicated specific fuel consumption (ISFC) of only 175 g/kW h, and a peak pressure rise rate of ,8 bar/deg.

Keywords: injection pressure, LTC, PCCI, diesel engine, combustion modelling

1 INTRODUCTION

In recent years, many advanced combustion strategies

have been proposed in order to meet current and

future emissions mandates. Most of the current

strategies can be lumped into the category of low-

temperature premixed combustion. Premixed com-

bustion is achieved by extending the ignition delay

such that the injection event has completed prior to

the start of combustion. Furthermore, by ensuring low

local equivalence ratios or by significantly reducing the

oxygen concentration by using high levels of exhaust

gas recirculation (EGR), flame temperatures can be

significantly reduced. Lower combustion temperatures

result in nitric oxides (NOx) reduction owing to the

high temperature dependency of NO kinetics. In ad-

dition, utilizing long ignition delay allows adequate

time for mixing prior to the start of combustion; thus,

rich regions are reduced and soot formation is in-

hibited. Furthermore, Akihama et al. [1] have shown

that when combustion temperatures are significantly

reduced, the conversion of polycyclic aromatic hydro-

carbons (PAH) to soot does not occur. Therefore, even

if rich regions are present, low soot levels can still be

achieved.

Although premixed combustion strategies have

been shown to yield very low NOx and soot levels

[2–4], several problems must be solved before these

strategies can see widespread use. The first problem is

spray–wall impingement. Because a premixed charge

is required, fuel must be injected early in the cycle to

allow adequate time for mixing; however, early cycle

charge densities are often low, which can result in

very long liquid lengths and spray impingement on

the cylinder liner. Another problem that must be

*Corresponding author: Engine Research Center, Department of

Mechanical Engineering, The University of Wisconsin, Madison,

1500 Engineering Drive, Madison, Wisconsin 53706, USA.

email: [email protected]

257

JER06409 Int. J. Engine Res. Vol. 11

https://www.researchgate.net/publication/228515967_A_study_of_methods_to_lower_HC_and_CO_emissions_in_diesel_HCCI?el=1_x_8&enrichId=rgreq-b4f618e4518dbb00708af51af91c0e99-XXX&enrichSource=Y292ZXJQYWdlOzIzODE4NjU3OTtBUzo5NzMwMjk5MTYwNTc2MkAxNDAwMjEwMjQ5MjEw

https://www.researchgate.net/publication/290832883_A_Study_of_the_Effects_of_High_EGR_High_Equivalence_Ratio_and_Mixing_Time_on_Emissions_Levels_in_a_Heavy-Duty_Diesel_Engine_for_PCCI_Combustion?el=1_x_8&enrichId=rgreq-b4f618e4518dbb00708af51af91c0e99-XXX&enrichSource=Y292ZXJQYWdlOzIzODE4NjU3OTtBUzo5NzMwMjk5MTYwNTc2MkAxNDAwMjEwMjQ5MjEw

addressed is combustion phasing control. Because

the ignition delay must be extended to allow adequate

time for mixing, injection timing can no longer be

used to control combustion phasing. Therefore,

chemistry time-scales must also be extended so that

ignition does not occur prematurely. Furthermore,

some mechanism must be employed to reduce the

rapid heat release associated with premixed combus-

tion strategies. Because the combustion rate is no

longer limited by the mixing process, as in conven-

tional diesel combustion, the heat release can be

rapid and can result in excessive engine noise and

structural damage.

Many researchers have addressed the issue of

spray–wall impingement associated with premixed

charge compression ignition (PCCI) type strategies.

Kawaguchi [5] proposed an injector capable of

changing the cone angle depending on the piston

position in order to maintain optimal spray targeting

over a wide range of injection timings. Ra and Reitz

[6] performed a numerical evaluation of the variable

geometry spray (VGS) concept and found wall film

levels at the time of ignition were reduced from 25

per cent for a fixed geometry nozzle to below 5 per

cent for the VGS case. They also evaluated a more

practical two-pulse spray concept which had two

different fixed spray angles, i.e. a narrow angle for

the first pulse and a wider angle for the second pulse,

and found similar wall film levels as the VGS case.

Kohketsu et al. [7] developed an injection system

that altered the pressure of the spray midstream by

using a solenoid to switch between low- and high-

pressure common rails, allowing for the first part of

the injection to be at low pressure and the second

portion at high pressure. Sun and Reitz [8] showed

the advantages of using low-pressure injections with

a fixed spray angle as a means for reducing spray

wall impingement for early injected fuel and pro-

posed an adaptive injection strategy (AIS) concept

which allows the use of low-pressure injections for

fuel injected early in the cycle and high-pressure

injection of fuel injected late in the cycle.

Combustion phasing control of premixed com-

pression ignition engines has received much atten-

tion in recent years. Many parameters have been

investigated for control of premixed combustion

phasing. Sjoberg and Dec [9] have shown repeatable

control of an homogeneous charge compression

ignition (HCCI) engine by tuning the intake tem-

peratures and EGR rate. Koci [10] and Opat et al. [4]

have shown very promising results by using very

high levels of EGR (,67 per cent) in order to reduce

oxygen content and control combustion phasing in a

PCCI engine. However, the use of extremely high

EGR moves the combustion regime near the misfire

limit and makes fuel distribution extremely impor-

tant to ensure that the little oxygen in the combus-

tion chamber is appropriately utilized. Park and

Reitz [11] investigated the low emissions window in

an HCCI engine and found that combustion phasing

could be suitably controlled by using variable valve

actuation (VVA) rather than excessively high EGR

levels. However, they showed that the use of VVA or

reduced intake temperatures had little influence on

the peak cycle temperature, and that peak cycle

temperature was primarily a function of EGR level.

Thus, relatively high levels of EGR must still be

employed in order to limit thermal NOx formation.

Kawano et al. [12] also explored the use of VVA as a

means of premixed combustion phasing control.

They found that by utilizing a late intake valve

closure time, combustion could be phased near top

dead centre (TDC). Furthermore, utilizing high levels

of EGR and a high-pressure injection system, NOx

and soot emissions could be reduced dramatically

below those of conventional diesel combustion.

Hasegawa and Yanagihara [13] explained the

uniform bulky combustion system (UNIBUS), which

involves a dual injection strategy. An early injection,

well before TDC (BTDC), is used to create a

premixed charge. Engine parameters (intake gas

temperature, intake valve closure (IVC) timing, fuel

quantity, and injection timing) were set such that

high-temperature heat release did not occur prior to

the start of the second injection. The second

injection occurs after TDC (ATDC) and is used to

control the start of combustion of the premixed fuel;

thus, unlike very early injection PCCI and HCCI

concepts, combustion phasing can be directly con-

trolled by the injection timing of the second pulse.

The UNIBUS concept was able to achieve low NOx

and near-zero soot emissions.

For premixed combustion strategies to gain wide-

spread acceptance the rate of heat release must be

controlled. Through a computational study, Sun and

Reitz [14] and Sun [15] proposed a two-stage

combustion (TSC) concept as a means for extending

the operating range of HCCI combustion. The first

stage is HCCI-like combustion and the second stage

is diffusion combustion under low oxygen concen-

tration conditions. Note that in the work of Sun and

Reitz [14], Sun [15], and in the current paper, the

terminology ‘cool flame reaction’ describes the low-

temperature reaction associated with forming a pool

of hydrogen peroxide which will later be consumed

in the high-temperature reaction. ‘First stage com-

258 S L Kokjohn and R D Reitz

Int. J. Engine Res. Vol. 11 JER06409

https://www.researchgate.net/publication/290678706_Adaptive_Injection_Strategies_AIS_for_Ultra-Low_Emissions_Diesel_Engines?el=1_x_8&enrichId=rgreq-b4f618e4518dbb00708af51af91c0e99-XXX&enrichSource=Y292ZXJQYWdlOzIzODE4NjU3OTtBUzo5NzMwMjk5MTYwNTc2MkAxNDAwMjEwMjQ5MjEw


bustion’ refers to high-temperature heat release of

an early injected or premixed fuel, and ‘second stage

combustion’ refers to combustion of a late injected

fuel. Computational work by Sun [15] showed that

optimized TSC could achieve NOx and soot levels at

the 2010 Environmental Protection Agency (EPA)

heavy-duty on-highway truck limits (0.1 and 0.01 g/

hp h of NOx and soot, respectively [16]) at 57 per

cent of full load while achieving a brake-specific fuel

consumption (BSFC) of close to 200 g/kW h. In an-

other computational study, Kokjohn and Reitz [17]

applied the TSC concept to a light-duty engine and

found that the rate of heat release and peak pres-

sure rise rate (PRR) could be controlled by the use of

optimized fuel splits, EGR, boost, and injection

timing.

Canova et al. [18] performed an experimental

investigation of a mixed-mode combustion strategy

similar to Sun’s TSC strategy in a 2.5 litre light-duty

engine. They used an external fuel atomizer to achieve

a homogeneous charge and a direct injection slightly

after TDC to extend the load limit of pure HCCI

combustion. At the 6.1 bar operating condition tested,

the mixed-mode combustion strategy showed simul-

taneous reduction in NOx and soot emissions com-

pared to conventional diesel combustion. Further-

more, cycle-to-cycle variability was found to be very

low, thus suggesting that mixed-mode combustion is a

stable operating strategy.

In a primarily computational study, Kokjohn et al.

[19] explored the combined use of VVA for combus-

tion phasing control and AIS (i.e. multiple injections

at different injection pressures) to reduce liner im-

pingement in an engine operating in PCCI mode. By

optimizing injection and operating parameters using

genetic algorithms (GAs), they were able to achieve

near-zero levels of NOx and net indicated specific

fuel consumption (ISFC) level of 185 g/kW h.

The main objective of the present study is to expand

on the optimization work of Kokjohn et al. [19] to

provide guidelines for solving problems with premixed

combustion strategies, namely, combustion phasing

control, excessive PRR, and spray–wall impingement

due to early injections. Operating conditions that will

simultaneously result in low noise, high efficiency, and

clean combustion are identified and investigated.

Additionally, trade-offs between noise, emissions, and

fuel consumption are examined.

2 ENGINE SPECIFICATIONS

The engine modelled in this study is a single-

cylinder version of the GM 1.9 litre engine with

specifications given in Table 1. This engine uses a

variable pressure pulse (VPP) injection system [19]

developed in the Diesel Emissions Reduction Con-

sortium (DERC) laboratory at the University of Wis-

consin-Madison’s Engine Research Center (ERC). The

VPP system, shown in Fig. 1, is capable of delivering

two independent injections at various pressures.

The first injection was designed to be at low pressure

(10–35 MPa), which has been shown to reduce wall

wetting of early cycle injections [8]. The second injec-

tion is at high pressure (80–120 MPa) to promote air

entrainment and droplet break-up.

The VPP system consists of a high-pressure com-

mon rail (HPCR), a low-pressure system (LPS), a

high-pressure solenoid, a recirculation tank, a needle

valve, and an adapter. The adapter houses the solen-

oid and pressure transducer and connects the HPCR,

LPS, and injector. The solenoid is initially turned

off for the low-pressure injection, allowing the LPS to

Table 1 Engine and injector specifications

Base engine type GM 1.9 litreBore 8.2 cmStroke 9.04 cmConnecting Rod Length 16.1 cmSquish height 0.0617 cmDisplacement 0.4774 litreCompression ratio 16.5:1Swirl ratio 2.2Bowl type ReentrantIntake valve opening 344u ATDC firingIntake valve closing 2132u ATDC firingExhaust valve opening 112u ATDC firingExhaust valve closing 388u ATDC firing

Injector type High-pressure solid coneManufacturer BoschInjection pressure 860 barIncluded angle 155uNumber of holes 7Nozzle hole diameter 141mm

Fig. 1 VPP system schematic diagram

Investigation of charge preparation strategies for controlled PCCI 259


control the pressure in the adapter. After the first

low-pressure injection, the solenoid opens, allowing

fuel from the HPCR to flow into the adapter. The

needle valve acts as a critical flow orifice, only

allowing a small amount of the fuel to pass into

the LPS. The resulting effect is a rapid increase of

pressure in the adapter. The second injection is

then fired, and the solenoid is turned to the off posi-

tion. The pressurized fuel bleeds through the needle

valve into the LPS.

3 COMPUTATIONAL MODEL

The computations were performed using the KIVA-

3v release 2 code [20] with improvements to many

physical and chemistry models developed at the ERC

[21–25]. The KIVA-3v code was coupled with the

CHEMKINTM II solver for detailed chemistry calcu-

lations. A 39-species and 131-reaction mechanism

for n-heptane [26] was used to simulate diesel fuel

chemistry. Soot was predicted using a two-step

phenomenological soot model [27] based on the

approach of Hiroyasu and Kadota [28]. The rate of

change of soot mass within a computational cell, _MM s,

is given by competition between formation, _MMsf , and

oxidation, _MM so, processes.

_MMs~ _MM sf{ _MMso ð1Þ

where the soot formation rate is given by the Arrhe-

nius expression

_MMsf~dMs

dt~Asf MC2H2

Pn expEsf

RT

� �ð2Þ

and the oxidation rate, RTotal, is given by the carbon

oxidation model of Nagle and Strickland-Constable

[29]. The oxidation rate considers two reaction sites

as described by Liu et al. [30], and

_MMso~dMso

dt~

6MWc

rsDsMsRTotal ð3Þ

Ms is the soot mass in the computational cell. The

soot density, rs, and soot particle diameter, Ds, were

taken as 2 g/cm3 and 0.025 micron, respectively.

The soot model used in the present study used

acetylene as an inception species, allowing coupling

of the soot model and chemistry solver through the

addition of 13 reactions involving acetylene. NOx

emissions are predicted using a reduced NO me-

chanism [15] consisting of four additional species

and 12 reactions. The reduced NO mechanism is

based on the Gas Research Institute (GRI) NO me-

chanism [31]. The reduced NO mechanism describ-

ed by Sun [15] is given by reactions (4) through

(15) below. Reactions (4) through (6) are the thermal

or extended Zeldovich NO pathways that dominate

at high temperatures [32]. Reactions 7 through 11

are low-temperature formation pathways and reac-

tions (12) through (15) represent the catalytic NO

formation cycle. The catalytic reaction pathways be-

come important when NO is recirculated into the

intake stream (i.e. EGR).

NzNOuN2zO ð4Þ

NzO2uNOzO ð5Þ

NzOHuNOzH ð6Þ

N2OzOuN2zO2 ð7Þ

N2OzOu2NO ð8Þ

N2OzHuN2zOH ð9Þ

N2OzOHuN2zHO2 ð10Þ

N2OzMuN2zOzM ð11Þ

HO2zNOuNO2zOH ð12Þ

NOzOzMuNO2zM ð13Þ

NO2zOuNOzO2 ð14Þ

NO2zHuNOzOH ð15Þ

As detailed chemistry calculations are often time

consuming, the adaptive multi-grid chemistry

(AMC) model of Shi et al. [33] was employed.

The current implementation of the CHEMKIN II

solver considers every grid cell as a well-stirred

reactor (WSR) and solves for the mass density of

each species in the continuity equation and the



reaction source term in the energy equation. The

AMC model reduces the number of cells that need

to be computed by the chemistry solver by group-

ing cells with similar gas properties for chemis-

try calculations and remapping the results to the

original mesh. A brief overview of the mapping

and remapping procedures is given below. More

information can be found in the work of Shi et al.

[33, 34]. Cells are grouped based on the so-called

progress equivalence ratio [35], which is defined

by

w~2C#

{CO2zH#

{H2O

.2{z’C#

{CO2

O#{CO2{H2O{z’C#

{CO2

ð16Þ

In this equation, z’ defines the proportion of fuel

oxygen to fuel carbon, and for hydrocarbon fuels

without oxygen, as is the case in this work, z’ is

zero. The superscript # denotes the number of

atoms of each species present in each computa-

tional cell. The equivalence ratio is defined based

on complete combustion, but the products CO2

and H2O are excluded. For example, C#{CO2

is given

by

C#{CO2

~Xn species

i~1

xiNcellCið Þ{xCO2NcellCCO2

ð17Þ

where xi is the mole fraction of species i, Ncell is

the total number of moles in the cell, Ci is the num-

ber of carbon atoms in species i (e.g. CCO25 1).

The complete combustion products (i.e. stoichio-

metrically saturated species) are excluded from

the progress equivalence ratio calculation based

on the findings of Babajimopoulos et al. [35]. They

showed that the evolution of the progress equivalence

ratio excluding CO2 and H2O (i.e. as defined by equ-

ation (16)) better corresponded to the evolution of the

heat release than the kinetic equivalence ratio in-

cluding these stoichiometrically saturated species.

Therefore, they concluded that the progress equiva-

lence ratio (as defined by equation (16)) is the most

appropriate metric for identifying cells with similar

compositions.

During stratified combustion events, significant

gradients in mass fraction of species are present.

Therefore, it is not appropriate to group all cells with

similar progress equivalence ratios. A better solution

is to group cells with similar thermodynamic proper-

ties and species composition. Accordingly, a tem-

perature inhomogeneity, sT, was used as an indicator

for assessing the grouping region adaptively, where

sT~

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n{1

Xn

i~1

Ti{�TT� �2

vuut ð18Þ

�TT denotes the average in-cylinder temperature and Ti

is individual temperature of each of the n cells. Each

group of cells is then sent to the chemistry solver as a

single cell and species are redistributed back to the

cells in the original mesh, such that the gas properties

of each cell remapped from the group would be

comparable with those if the chemistry of each cell

was solved individually. By using the AMC model,

computational times can be reduced by more than a

factor of three while maintaining an accurate predic-

tion of the emissions results [34].

The spray model employed in this study uses the

Lagrangian-drop and Eulerian-fluid (LDEF) approach.

Because a detailed chemistry model is used, it is desir-

able to use a relatively coarse computational mesh;

however, severe grid size dependency has been ob-

served in LDEF spray models. This problem is most

severe in the near-nozzle region where the droplets are

very close together and occupy only small portions of

the Eulerian mesh. Abraham [36] showed that accurate

modelling of the near-nozzle region required grid

resolution on the order of the orifice diameter.

However, it is not feasible from a computational time

standpoint to solve engine problems on such a fine

mesh. Furthermore, a fundamental assumption of the

LDEF approach is that the volume fraction of droplets

in each cell is small, i.e. the void fraction is near one.

Thus, if the mesh size is overly refined (i.e. mesh size of

the order of the droplet size), this assumption may be

violated. In order to reduce the grid size dependency of

the LDEF spray model and allow accurate spray

simulation on a relatively coarse grid, the Gasjet model

of Abani et al. [22] is employed to model the relative

velocity between the droplets and gas phase in the

near-nozzle region. Their approach assumes that the

relative velocity between a droplet and the gas phase is

equal to that between the droplet and a turbulent gas

jet with the same mass and momentum as that of the

injected fuel. This approach imposes an axial compo-

nent for the gas phase velocity as a function of distance

from the nozzle, which is used in the droplet

acceleration equation given by

dU

dt~

3

8CD

rg

rlrdU{V gas

�� U{V gas

� �ð19Þ

where U is the droplet velocity vector, CD is the

droplet drag coefficient, which is a function of



https://www.researchgate.net/publication/245326134_An_adaptive_multi-grid_chemistry_AMC_model_for_efficient_simulation_of_HCCI_and_DI_engine_combustion?el=1_x_8&enrichId=rgreq-b4f618e4518dbb00708af51af91c0e99-XXX&enrichSource=Y292ZXJQYWdlOzIzODE4NjU3OTtBUzo5NzMwMjk5MTYwNTc2MkAxNDAwMjEwMjQ5MjEw


Reynolds number, rg and rl are the gas and liquid

phase densities, respectively, rd is the droplet

radius, and Vgas is the gas phase velocity vector

given as V gas~ Vx, Vy , Vz

� �, where Vy and Vz are

perpendicular to the spray axis and are obtained

from the Eulerian gas phase solution, and Vx, the

axial component of the gas phase velocity, is found

from gas-jet theory [37] as

Vx~ min Uinj,

3Uinjdnoz

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffirl

.rg

� rKentrx

2664

|1

1z 12r2= K 2entrx

2ð Þð Þ½ �2

( )ð20Þ

where Uinj is the injection velocity, dnoz is the nozzle

diameter, Kentr is a model constant taken to be 0.7

as suggested by Abani et al. [21], x is the position

downstream of the nozzle on the spray axis, and r is

the radial position.

Droplet break-up is modelled using the hybrid

Kelvin–Helmholtz (KH)–Rayleigh–Taylor (RT) model

described by Beale and Reitz [23]. The droplet collision

model is based on O’Rourke’s model; however, a radius

of influence method is used to determine the possible

collision partners to further reduce mesh dependency

[22]. In addition, the collision model was expanded by

Munnannur and Reitz [38] to include a more compre-

hensive range of collision outcomes. The current

implementation of the droplet collision model con-

siders bounce, coalescence, and both fragmenting and

non-fragmenting separations. Droplet interactions with

the wall are considered through a wall film submodel

[39, 40], which includes the effects associated with

splash, film spreading, and motion due to inertia.

The computational grid used in this study was a

51.4u sector mesh that represents a single nozzle hole

of the seven-hole Bosch injector used in the engine.

Abani et al. [21] and Shi et al. [34] have shown the

improved spray models used in this study yield

similar results on both fine and coarse grids. There-

fore, to reduce computational expense further, the

coarse grid of Fig. 2 was used for parameter sweeps

and the final solution was verified on the fine grid.

4 MODEL VALIDATION

To ensure the models were predictive, two sets of

model validation were performed. In the first part,

model predictions are compared to the single injec-

tion PCCI experiments of Opat et al. [4]. In the second

part, model predictions are compared to engine

experiments of Kokjohn et al. [19] using the VPP

injection system and a multiple injection strategy.

4.1 Single injection PCCI combustion validation

The engine experiments performed by Opat et al. [4]

used for computational model validation were con-

ducted using a single-injection PCCI approach at the

same operating condition as that investigated in the

present study (5.5 bar nominal indicated mean effec-

tive pressure (IMEP) and 2000 r/min). To achieve

PCCI combustion, Opat utilized an early injection to

attain adequate mixing prior to the start of combus-

tion and a high EGR rate (,67 per cent) to suppress in-

cylinder temperatures and extend the ignition delay.

Model validation was performed over a start-of-

injection (SOI) sweep from 21 to 39u BTDC. Figure 3

shows the cylinder pressure and heat release rate

comparison from a representative case with an SOI

of 31u BTDC. The locations of the cool flame and

main heat release are predicted very accurately;

however, the simulation predicts two peaks on the

Fig. 2 Computational grids shown at TDC. The gridsare 51.4u sector meshes consisting of 11 000 and30 000 cells at BDC for the coarse and fine grids,respectively

Fig. 3 Comparisons of computed and measured cy-linder pressure and heat release rate. The SOItiming was 31u BTDC






high-temperature heat release, which was not

observed in the experiments. It is thought that the

two-peak behaviour is due to competition for OH

radicals between the CO + OH R CO2 + H reaction

and other less exothermic reactions. That is, as OH

radicals are consumed by reactions other than the

CO + OH R CO2 + H reaction, the high-temperature

heat release slows down. Meanwhile, CO continues

to accumulate and is finally consumed when a

sufficient concentration of OH radicals is formed

through chain branching reactions. The two-peak

behaviour can be eliminated by adjusting the rates of

the competing reactions; however, the current n-

heptane mechanism generally performs better under

low-temperature combustion conditions than the

modified version (Y. Ra, 2008, personal communica-

tion). Additionally, the differences between the

measured and predicted cylinder pressure may be

because n-heptane is used to represent the kinetics

of diesel fuel. That is, some differences are expected

owing to the absence of the aromatic compounds

present in real diesel fuel. Although some differences

exist, the CFD predicted pressure traces are con-

sidered to be in acceptable agreement with the

measurements for the purposes of the present study.

Figure 4(a) shows comparisons of predicted and

measured soot and NOx emissions. The simulation

soot results do not show as much sensitivity to start-of-

injection timing as the experimental data. For exam-

ple, at an SOI of 237u ATDC the experiments show a

sharp increase in soot levels; however, the simulation

results show a flattening out of the soot versus SOI

curve. In addition, at an SOI of 222 the experiments

show a spike in soot which is not captured by the

simulations. Nevertheless, at this condition, the soot

trends and magnitudes were predicted reasonably

well. The simulation captures all of the NOx trends

exhibited by the experimental data; however, the

results show the simulation slightly under-predicts

NOx for all injection timings. This discrepancy may be

due to uncertainties in the initial NO concentration in

the EGR gases; however, since the trend is captured

well, further tuning was not explored.

Figure 4(b) shows comparisons of computed and

measured CO and unburned hydrocarbon (UHC)

emissions. The CO curve shows very accurate pre-

diction of both the trends and magnitude of CO

emissions for all but the most advanced injection

timings. The location of the CO minimum is captured

well. Opat et al. [4] suggest the CO minimum is the

result of optimum spray targeting. The correct UHC

trend is captured; however, UHC is over-predicted at

all injection timings. One possible explanation for the

over-prediction of UHC emissions is uncertainties in

the identity of the UHC species. The experimental

UHC emissions were collected using a flame ioniza-

tion detector (FID) and converted to the emissions

index values by assuming the hydrocarbons are of

the form CHb, where b is the hydrogen-to-carbon

ratio of the fuel. In this case, b was 1.86. Therefore, all

measured hydrocarbon species were assumed to have

a molecular weight of 13.9 kg/kmol. In comparison,

the simulations include the molecular weight of all

calculated hydrocarbon species. Dominant species in

the exhaust gases of the simulations include methane,

ethane, propylene, formaldehyde, and propyne. The

contribution of these species results in an average

molecular weight for the hydrocarbon species of ap-

proximately 20 kg/kmol. Furthermore, Fourier trans-

Fig. 4 (a) NOx and soot emissions and (b) CO andUHC emissions computed by KIVA comparedwith measured values over an injection timingsweep




form infrared spectroscopy (FTIR) measurements

performed by Koci [10] concluded that the dominant

hydrocarbon species in this condition are methane,

ethylene, and acetylene. These species all have mole-

cular weights greater than the assumed value of

13.9 kg/kmol; therefore, it seems reasonable to con-

clude that some of the over-prediction in hydro-

carbon emissions is due to uncertainties in the mole-

cular weight of UHC species.

In general, the comparisons between the simula-

tions and experiments show reasonable agreement.

However, it must be noted that representing the

multi-component kinetics of diesel fuel with a single

straight-chain hydrocarbon likely introduces inac-

curacies. However, numerous studies (e.g. Kong et

al. [27]) have shown that this approach is accep-

table for engineering approximations. Additionally,

in this work, the physical properties (for spray and

mixing processes) of the diesel fuel were represented

by tetradecane. Of course, the multi-component

vaporization of the diesel fuel used in the validation

experiments is not captured by this approach (i.e.

the distillation curve of diesel fuel is not repro-

duced). It is possible that the differences between

single- and multi-component vaporization result in

differences in the fuel distribution prior to and

during combustion. PCCI combustion has been

shown to be very sensitive to the mixture prepara-

tion details (e.g. Opat et al. [4]); therefore, the

simplified vaporization model should be borne

in mind when accessing the observed differences

between the simulations and experiments. However,

this approach has been used in numerous studies

and has been shown to yield acceptable results. For

example, Shuai et al. [41] compared model predicted

fuel distributions to fuel distributions acquired using

planar laser-induced fluorescence (PLIF) in an

optically accessible engine operating in a low-

temperature combustion mode. They showed that,

under low-temperature combustion conditions, the

spray models used in the current study accurately

reproduced the measured fuel distribution.

4.2 Split-injection validation using the VPPsystem

To ensure that the models are able to predict ade-

quately the emissions and performance characteristics

using the VPP system, the engine experiments of

Kokjohn et al. [19] – performed using multiple

injections at different injection pressures – were used

for further model validation. The engine was operated

at the condition of interest for the present study –

5.5 bar nominal IMEP (14.9 mg/stroke) and 2000 r/min.

However, to accommodate concurrent research pro-

jects at the ERC, the engine geometry for model

validation using the VPP system is slightly different

from the specifications given in Table 1. To highlight

the geometrical differences, Fig. 5 shows the compu-

tational grid used for comparison with the split-

injection experiments using the VPP system and the

coarse mesh of Fig. 2. The operating conditions are

given in Table 2 and the changes to the engine

geometry and injector for the model validation

experiments are given in Table 3.

Figure 6 shows typical cylinder pressure and heat

release rate comparisons for each of the validations

cases. The predicted cylinder pressures and heat

Fig. 5 (a) Computational grid used for comparisonswith the experimental data using the VPPinjection system. (b) Computational grid usedfor all other investigations (shown to highlightthe differences in engine geometry). Note thatthe bowl shape of the grid shown in (b) isidentical to that of Fig. 2

Table 2 Operating conditions for model validation

Singleinjection

Splitinjection

Engine load (bar net IMEP) 5.5Engine speed (r/min) 2000Inlet pressure (bar) 1.62Inlet temperature (uC) 65Swirl ratio 2.2First pulse injection pressure (bar) 860 bar 300First pulse injection timing (uBTDC) 16 to 34 52First pulse injection duration (uCA) 8.6 7.2Second pulse injection pressure (bar) NA 1200Second pulse injection timing (uBTDC) NA 4 to 0Second pulse injection duration (uCA) NA 7.84Fuel mass (mg/stroke) 15 14.7Fuel split (% of fuel in pulse 1) 100 20EGR rate (%) 67 50

Table 3 Differences in engine specifications formodel validation experiments. Valuesshown in parentheses are used for theoptimization study

Squish height 0.11 (0.0617) cmCompression ratio 15.5:1 (16.5:1)Bowl type Mexican-hat (re-entrant)Manufacturer Denso (Bosch)Number of holes 8 (7)Nozzle hole diameter 128 (141) mm




release rates agree well with the experimental

results. Figure 7 shows comparisons of predicted

and measured emissions. The simulations capture

the NOx trends exhibited by the experimental data;

however, the results show that the simulation

slightly under-predicts NOx for all injection timings.

Again, this may be due to uncertainties in the initial

conditions (e.g. IVC temperature and composition).

The correct UHC trend is captured; however, in

contrast to the single-injection PCCI comparisons,

UHC is under-predicted at all injection timings. The

differences in the measured and simulated UHC

emissions may be due to the simplified crevice

geometry used in the computational work. The

crevice grid used in the simulations does not

consider any of the details of the crevice geometry

(e.g. ring grooves and ring motion were neglected).

Thus, changes in transport to and from the crevice

region resulting from these features are not con-

sidered. Furthermore, crevice volumes of the head

gasket, injector, and pressure transducer are not

considered. Although the modelling study included

geometrical simplifications that may affect the

magnitude of UHC, the correct UHC trend is

captured by the simulations; thus, it is thought that

the simulations will adequately provide at least

qualitative information on the UHC response to

changing engine parameters.

The magnitudes of soot and CO emissions are

captured well by the model; however, a discrepancy

in the trends exists. At a second injection timing of

24u ATDC, the simulations show a decrease in soot

emissions, while the experiments show soot remain-

ing relatively constant. This discrepancy is also

observed in CO emissions predictions. Thus, some

caution must be exercised when interpreting the soot

and CO emissions results presented later in the paper.

5 SELECTION OF BASELINE CASE

This paper investigates the effects of design para-

meters on controlled, partially premixed combus-

tion. Before this investigation, it is of interest to

revisit the selection of the baseline operating condi-

tion used for this study. Kokjohn et al. [19] used a

multi-objective genetic algorithm (MOGA) coupled

with the KIVA-CHEMKIN code to explore PPCI

combustion. The baseline condition for this study

was selected from the optimization solutions of

Kokjohn et al. [19]. A genetic algorithm (GA) is a

search technique inspired by the Darwinian idea of

‘survival of the fittest’ [42]. The GA technique can be

summarized as follows.

1. An initial population is generated by randomly

assigned values for each optimization parameter

to a specified number of optimization citizens.

2. The ‘fitness’ of each citizen is evaluated by

performing experiments or simulations.

3. Citizens with high ‘fitness’ are allowed to repro-

duce, resulting in a new generation of citizens. At

this point, changes in a portion of the population

may be introduced via mutations.

Fig. 6 Comparisons of computed and measured cy-linder pressure and heat release rate

Fig. 7 NOx, soot, UHC, and CO emissions computedby KIVA compared with measured values overan injection timing sweep



4. Successive generations are performed until the

goals of the optimization are attained.

The multi-objective approach was favoured over the

use of a merit function based single-objective optimi-

zation scheme in order to generate a set of solutions

(i.e. Pareto optimal solutions) from which the engine

designer may choose an in-cylinder strategy that best

matches the available aftertreatment system. A solu-

tion is defined as Pareto optimal if there are no other

solutions that better minimize all objectives. This

thought is shown graphically by Shi and Reitz [43] (see

Fig. 8) for a two-objective minimization problem. It is

observed that no solutions outperform A–D in the task

of minimizing both objectives simultaneously. In other

words, points A–D are not dominated by any of the

other solutions, E–H; thus, these non-dominated

solutions are termed Pareto optimal. The set of non-

dominated solutions makes up a Pareto front, as

depicted in Fig. 8.

The computational optimization considered six

objectives (NOx, soot, CO, UHC, ISFC, and peak PRR)

and seven parameters (IVC timing, EGR rate, fuel

split, early injection timing, late injection timing,

early injection pressure, and late injection pressure).

Table 4 shows the parameters and ranges used to

select the baseline case. Note that in the optimiza-

tion of Kokjohn et al. [19], the trapped mass was held

fixed at 0.84 g by varying the intake pressure with

IVC timing and EGR. That is, for 0 EGR at IVC

timings of 280u and 2180u ATDC, the intake

pressure needed to achieve 0.84 g of trapped mass

was 2.0 and 1.6 bar, respectively. Additionally, in the

optimization of Kokjohn et al. [19] and the present

work the EGR was assumed to consist only of N2, O2,

CO2, and H2O. The optimization ran for 13 genera-

tions with a population size of 24, giving a total of

312 runs. During the optimization, convergence was

monitored by comparing the location of the current

Pareto front to the location of all other Pareto

solutions. The optimization appeared to have con-

verged after ,7 generations. Thus, from generation

7 to 13 the solutions were ‘populating’ the current

Pareto front, rather than generating new Pareto

fronts. The population size was set based on the

recommendations of Shi and Reitz [43]. Each

run took about 25 h on a 3.0 GHz AMD AthlonTM

processor and each generation was processed in

parallel. Because the optimization contained six

unique objectives, the Pareto front contained the

set of solutions that minimized each of these six

objectives differently. The overall Pareto front con-

sisted of 133 designs with varying NOx, soot, UHC,

CO, ISFC, and peak PRR performance. For this work

a low-NOx, low-soot, high-efficiency Pareto solution

was selected for further analysis. The parameters

and results of this solution, along with the target

values, are given in Tables 4 and 5, respectively.

Target values were selected based on the work of

Cooper et al. [44] and the US Tier 2 Bin 5 regulations.

Fig. 8 Illustration of the concept of Pareto optimality [43]

Table 4 Optimization parameters and ranges ofKokjohn et al. [19] and parameters of theselected Pareto solution. Note that the IVCtiming and intake pressure were adjustedsimultaneously to maintain a fixed trappedmass of 0.84 g. That is, at IVC timings of 280uand 2180u ATDC the intake pressure neededto achieve this trapped mass was 2.0 and1.6 bar, respectively

OptimizationRanges

Selectedcase

Early injection pressure (bar) 100 to 1500 563 barLate injection pressure (bar) 600 to 1500 1380 barEarly injection timing (uATDC) IVC – (SOLI-30) 243Late injection timing (uATDC) 210 to 25 21.3Fuel split (% of total fuel) 10 to 90 30IVC timing (uATDC) 280 to 2180 2104Intake pressure (bar) 1.6 to 2 1.74EGR rate 0 to 67 % 54 %

Table 5 Objectives of selected case from Paretofront for computational GA optimization

Selected Target

NOx (g/kgf) 0.29 0.4Soot (g/kgf) 0.11 0.2HC (g/kgf) 48 30CO (g/kgf) 118 100Net ISFC (g/kW h) 185 190Peak PRR (bar/deg) 7.7 5.5



6 RESULTS AND DISCUSSION

The GA is an excellent tool for exploring the design

space. However, when many objectives and para-

meters are used, it is often difficult to interpret the

results, and in the interest of reduced computing

time, the Pareto front is left sparse. Consequently, to

understand the effects of influential design para-

meters and to see if further improvements can be

made to the Pareto solutions, the design space was

further explored through a parametric study. In this

work the effects of first and second pulse injection

pressure and timing, spray targeting, and swirl ratio

were investigated.

The following sections highlight the important

effects of each design parameter on emissions and

performance. Each sweep shows the effect of a single

parameter on emissions and performance with all

other parameters held fixed at the values given in

Table 4. By varying a single parameter, the effect of

each parameter on emissions and performance is

isolated. However, the weakness to this approach is

that interactions between variables, which may be

important for further reducing emissions and im-

proving performance, are not identified. This fact

should be kept in mind when interpreting the

results. Each section begins with a general discus-

sion of the effect of each design parameter, identifies

important aspects, and provides analysis of the root

cause of performance and emissions trends. The

final section combines the work of the computa-

tional optimization and parameter sweeps to choose

cases that best meet the target emissions and

performance characteristics.

6.1 First pulse injection pressure sweep

Figure 9 shows the effects of the first pulse injection

pressure on engine performance and emissions. For

this case, the first pulse injection pressure had little

effect on the emissions or fuel consumption. The

insensitivity of emissions levels to first pulse injec-

tion pressure is due to the small quantity of fuel

injected in the first pulse. Because the fuel quantity

was small, liquid penetration was short and spray

impingement on the cylinder liner was not observed,

even when the injection pressure was high. In

contrast to the present findings, Kokjohn et al. [19]

and Kokjohn and Reitz [45] showed that when the

interaction between fuel split (i.e. fraction of fuel

injected in the first pulse) and first pulse injection

pressure was considered, significant fuel consump-

tion benefits were achieved by utilizing a low-

pressure early cycle injection. To illustrate this point,

a non-parametric regression technique, the compo-

nent selection smoothing operator (COSSO) of Lin

and Zhang [46] was used to analyse the results of

MOGA optimization of Kokjohn et al. [19]. Figure 10

shows the response surface of fuel consumption as a

function of fuel split (frac) and first pulse injection

pressure. It can be seen that at high first pulse

injection pressures and high levels of fuel in the first

pulse, fuel consumption increases significantly from

the baseline value (shown by the star). Thus, it

appears that reduced first pulse injection pressure

Fig. 9 Effect of first pulse injection pressure on engineperformance and emissions. The stars indicatethe parameters chosen by the GA and the solidline shows the target values. The pumping loopfor calculation of net ISFC was modelled usingcycle simulations



provides improved flexibility in the quantity of fuel

injected early in the cycle.

Although, fuel consumption benefits of the low-

pressure injection were not observed, owing to the

small quantity of fuel in the first pulse, Fig. 9 shows

that reducing the first pulse injection pressure from

the GA selected value of 560 bar to 100 bar, results in

a decrease in peak PRR from 7.7 to 4.1 bar/deg with

only a slight increase in fuel consumption (less than

1 per cent). To understand the cause of the reduction

in engine noise (peak PRR (note that in this work

peak PRR was used as an indication of engine noise

and it has been found that, at this condition, a peak

PRR of 5.5 bar/deg corresponds to a measurement of

,89 dB on an AVL noisemeter [10])) when first pulse

injection pressure was reduced, the heat release

rates are compared for cases with 100 and 1000 bar

first pulse injection pressures, as shown in Fig. 11.

Figure 11 shows that the 1000 bar injection pres-

sure case has a small high-temperature heat release

prior to the start of the second injection. This heat

release increased temperatures slightly and provided

a pool of radicals, which advanced the timing of the

main heat release, and increased the peak PRR. Since

only a small fraction of the total fuel was injected

early in the cycle (,30 per cent), if the charge were

perfectly mixed the equivalence ratio would be too

lean to support combustion. Therefore, in order for

combustion to occur prior to the second injection,

the mixture must be sufficiently stratified in order to

provide regions of suitably high equivalence ratio.

This result suggests that reducing the injection

pressure of the first injection enhanced mixing of

the early-injected fuel. The enhanced charge pre-

paration with reduced injection pressure (of the

early cycle injection) can be attributed to a combi-

nation of extended injection duration that allowed

fuel to target a wider range of piston locations and

reduced injection velocity that allowed the spray to

be more influenced by the bulk swirl. For example,

when the injection duration is extended and the

injection velocity is reduced – by reducing the

injection pressure – the first part of the injection

event can target the squish region, while the latter

portion of the injection event can target the bowl

region in order to distribute droplets optimally to

create a well-mixed charge. This finding is supported

by the experiments of Swor [47], where the perfor-

mance of a two-spray angle nozzle (one nozzle

targeting the squish and one targeting the bowl) and

low-pressure early cycle injection were found to be

very similar. Furthermore, it appears that the

increased momentum of the high-pressure injection

caused the spray to be less influenced by the swirl,

thus creating locally rich regions that would support

combustion even with the small quantity of fuel

injected in the first pulse.

To understand the effects of injection pressure on

fuel distribution, the maximum equivalence ratio for

the 100 and 1000 bar cases are plotted and in-

cylinder images of equivalence ratio are shown in

Figure 12. Although, the differences in injection

duration are relatively small (6.5u crank angle (CA)

and 4.4 uCA for the 100 bar and 1000 bar injection

pressures, respectively), it can be seen that the

differences in the final fuel distribution are signifi-

cant. For these cases, the SOI was held fixed at

Fig. 10 COSSO generated response surface from theoptimization data of Kokjohn et al. [19]showing fuel consumption (ISFC) as a func-tion of fraction of fuel in the first pulse (frac)and first pulse injection pressure. The starindicates the baseline operating conditions

Fig. 11 Heat release rate for cases with first pulseinjection pressures of 100 and 1000 bar



43uBTDC; therefore, both cases initially target the

squish region. However, the reduced momentum

and increased injection duration of the low-pressure

injection allows the swirl to reduce the spray

penetration and promote a more uniform charge

that is primarily constrained to the bowl. In contrast,

the plot of maximum equivalence ratio shows that

the 1000 bar case has a significantly higher max-

imum equivalence ratio prior to the onset of

combustion. The in-cylinder images of the 1000 bar

case show that the richest region is located in the

squish area where fuel impinged on the piston

surface from the first injection. The images of the

100 bar case show a very uniformly mixed charge

(W, 0.4) from the bowl lip to the near-nozzle region.

The reduced equivalence ratio for the 100 bar case

caused a slight delay in the combustion phasing and

reduced the peak PRR. It should be noted, however,

that since the improvement in peak PRR came at the

expense of a slight increase in ISFC (due to slightly

delayed combustion phasing), reducing the injection

pressure from 563 to 100 bar represents a movement

along the current Pareto front rather than the

creation of a new Pareto front.

6.2 Second pulse injection pressure sweep

Figure 13 shows the effect of second pulse injection

pressure on performance and emissions. As the

second pulse injection pressure was increased from

500 bar to 1300 bar, significant reductions in soot,

UHC, CO, and fuel consumption were observed.

However, these reductions came at the expense of

increased NOx and peak PRR. As the second pulse

injection pressure was further increased past 1300 bar,

further reductions in soot, UHC, CO, and fuel

consumption were observed, albeit significantly less

than the reductions between 500 and 1300 bar.

Increasing the injection pressure resulted in a

significant increase in the rate of pressure rise. To

understand the source of the increased rate of

pressure rise, the heat release rates and pressure

traces were compared for 500, 1300, and 1800 bar

injection pressures, as shown in Fig. 14. It can be

seen that the heat release for the 1800 bar case

occurs ,3 CA degrees before the heat release of the

1000 bar case and ,6 CA degrees before the heat

release of the 500 bar case, thus explaining the

increase in peak PRR. The higher injection pressure

allowed the duration to be shortened by ,1.5 CA

degrees from the 1000 bar case and 3.8 CA degrees

from the 500 bar case; thus the advance in heat

release is partially due to reduced injection duration,

Fig. 12 Maximum equivalence ratio in the combustionchamber and contours on a cut plane coin-cident with the spray axis for cases with firstpulse injection pressures of 100 and 1000 bar

Fig. 13 Effect of second pulse injection pressure onengine performance and emissions. The starsindicate the parameters chosen by the GA andthe solid line shows the target values



i.e. advanced end-of-injection. The remainder of the

increased peak pressure rise with increased injec-

tion pressure is likely to be due to enhanced fuel

preparation owing to smaller, easier to vaporize,

droplets created because of improved break-up from

higher injection velocity.

Soot is significantly reduced as the injection

pressure is increased. To understand the source of

the soot reduction with increasing injection pres-

sure, the soot locations are compared between two

cases (500 and 1000 bar second pulse injection

pressures). Figure 15 shows the soot histories and

cut-planes coloured by soot mass fraction for the

two cases. First, from the soot histories it can be seen

that the 500 bar case forms only slightly more soot,

but oxidation is significantly reduced compared to

the 1000 bar case. Comparing the locations of the

soot clouds (the grey iso-surfaces of Fig. 15), it can

be seen that the locations of the soot clouds are

significantly different. Specifically, the soot cloud of

the 500 bar case is located primarily in the squish

region, while the soot cloud for the 1000 bar case

is distributed between both the squish and bowl

regions. Because the end-of-injection for the 500 bar

case is ,2.4 CA degrees later than the 1000 bar

injection pressure case; the spray targeting is slightly

higher on the piston bowl (i.e. closer to the bowl lip).

The difference in spray targeting causes more fuel to

enter the squish region – due to reverse squish flow –

and results in less access to the available oxygen in the

bowl for the 500 bar case than the 1000 bar case, thus

explaining the reduced soot oxidation as the injection

pressure is reduced. Additionally, soot formation may

be reduced due to the increased oxygen entrainment

resulting from higher injection pressures. Moreover,

the higher injection pressures may also enhance the

mixing rate and soot oxidation after the end-of-

injection. It appears that, in this case, increasing the

injection pressure reduced soot emissions by enhan-

cing oxidation through improved spray targeting and

reduced soot formation through improved charge

preparation due to enhanced droplet break-up and

increased mixing. These findings suggest that a higher

second pulse injection pressure is beneficial for soot

reduction, but another mechanism must be employed

to maintain low engine noise.

6.3 First pulse injection timing sweep

Figure 16 shows the effect of the first pulse injection

timing on engine performance and emissions. Re-

tarding the injection timing beyond the value selected

by the optimization appears to have little effect on

emissions or performance. This is likely to be due to

the small quantity of fuel injected in the first pulse.

Fig. 14 Cylinder pressure and heat release rate forcases with second injection pressures of 500,1000, and 1800 bar

Fig. 15 In-cylinder soot histories and contours on cutplanes coincident with the spray axis colouredby soot mass fraction. The dark grey iso-surface (at a soot mass fraction of 1.061025)shows the location of the soot cloud



Advancing the injection timing resulted in an increase

in UHC and ISFC. The similar CO levels of the two

cases suggest that most of the UHC increase was

the result of increased fuel in a crevice or fuel film

layer. That is, fuel that remained in regions of the

combustion chamber where the temperatures were

too cool to convert hydrocarbons to CO. Homoge-

neous reactor simulations performed by Kim et al.

[48], showed that significant levels of CO were not

seen until the temperature reached ,1000 K. Since

the crevice between the piston and cylinder liner has

a large surface area-to-volume ratio, the temperature

of the mixture in the crevice region remains nearly

isothermal at the wall temperature (,500 K).

To ensure that fuel missing the bowl and travelling

to the liner was the source of the observed increased

UHC emissions for the SOI 275uATDC case, UHC

distributions for the SOI1 243 and 275uATDC are

presented in Fig. 17. Both injection timings show a

region of relatively high UHC emission in the

centreline. The equivalence ratio distribution sug-

gests that UHC in the centreline region was due to

an overly lean region where UHC oxidation rates

were very low. Furthermore, the temperature in the

centreline region for both cases remains below

,1100 K throughout the cycle. The next source of

UHC emissions, the crevice region, was only present

in the SOI1 275uATDC case. Fuel injected early in

the cycle that travelled to the liner region was forced

into the crevice during the combustion process.

Because temperatures in the crevice remain very low

and limited oxygen is available in the squish region

to oxidize reverse crevice flow, this region became

a major source of UHC emissions for the SOI1

275uATDC case.

6.4 Second pulse injection timing sweep

Figure 18 shows the effect of second pulse injection

timing on engine performance and emissions.

Retarding the injection timing past the value

selected by the optimization (shown by the star on

Fig. 18) resulted in a rapid decrease in NOx and peak

PRR and increases in CO, UHC, and fuel consump-

tion. The dependence on peak PRR was expected

since the start of the high-temperature heat release

is controlled by the second injection timing in this

UNIBUS-like combustion strategy; thus, retarding

the second pulse injection timing resulted in delayed

combustion phasing and lower rates of pressure rise.

Fig. 16 Effect of first pulse injection timing on engineperformance and emissions. The stars indicatethe parameters chosen by the GA and the solidline shows the targets for each objective

Fig. 17 UHC distributions on a cut-plane coincidentwith the spray axis at 218uATDC for first pulseinjection timings of 243u and 275uATDC



Furthermore, as the second injection timing was

retarded, combustion occurred very late in the cycle,

thus explaining the reduced combustion efficiency.

As the injection timing was advanced from the

optimization value to 10u BTDC, NOx and peak PRR

increased while other performance and emissions

parameters remained relatively constant. Further

advancing the second pulse injection timing from 10

to 17.5u BTDC resulted in a continued increase in NOx,

while the peak PRR remained relatively constant.

Figure 19 shows combustion phasing (CA50) and

ignition dwell (defined in this study as CA50 to end

of injection (EOI)) as a function of second pulse

injection timing and the cylinder pressure and heat

release rates for each case. Figure 19 shows that

injection timings between 10u BTDC and TDC have

very similar ignition dwells, thus suggesting that

mixing time is relatively constant for these cases.

Furthermore, for injection timings between 10uBTDC and TDC, there is a strong correlation

between injection timing and combustion phasing,

showing that adequate control over the combustion

process is retained. As the injection timing was

advanced past 10u BTDC, the ignition dwell began to

increase; however, combustion phasing was still

correlated with injection timing until the second

pulse injection timing was advance past 15u BTDC.

At this point further injection timing advance did not

Fig. 18 Effect of second pulse injection timing onengine performance and emissions. The starsindicate the parameters chosen by the GAand the solid line shows the targets for eachobjective

Fig. 19 (a) Effect of second pulse injection timing onCA50 and ignition dwell (defined in this workas CA50 – EOI). (b) Cylinder pressure and rateof heat release for each second pulse injectiontiming. The numbers on the plot correspondto the second pulse injection timing in CAdegrees ATDC



significantly advance CA50, suggesting injection

timing and combustion phasing were decoupled.

This investigation suggests that near TDC second

pulse injections are capable of achieving low NOx

and acceptable PRR, while retaining significant

control over the phasing of the high-temperature

heat release. Thus, it appears the injection timing

selected by the MOGA optimization of Kokjohn et al.

[19] is a good choice for further work.

6.5 Injector included angle sweep

Figure 20 shows the effect of injector spray included

angle on engine performance and emissions. The

baseline included angle was 155u. Reducing the

included angle from the baseline value resulted in

an increase in NOx and peak PRR and reductions in

soot, UHC, CO, and ISFC. However, when the

included angle was reduced below 90u, significant

increases in soot, UHC, CO, and ISFC were observed.

The ISFC trend has the same shape as the CO and

UHC trends, while the NOx trend appears to be

inversely related to the CO and UHC trends. This

observation is as expected since enhancing CO

oxidation releases more energy, thus increasing work

output (i.e. reducing ISFC) and temperature (i.e.

increasing NOx). Since it appears that the trends are

dependent on CO and UHC oxidation, the focus of

this section is to explain the observed trends in CO

and UHC. Figure 21 shows contours of CO for each

case. The UHC distribution was similar to the CO

distribution; therefore, only CO contours are pre-

sented. Note that the 80u included angle case had

very high UHC and CO emissions, thus in order to

show changes for the 120u and 155u cases, the CO

contours in the bowl for the 80u case are saturated.

From Fig. 21 it can be seen that for the 120u and 155ucases, most CO is located in the cylinder centreline

region. The 80u included angle case also shows CO

and UHC in the centreline, but the highest concen-

trations exist in the bowl.

In general, UHC and CO emissions are the result

of either rich combustion, where sufficient oxygen is

not available to oxidize the reactants completely, or

lean regions, where reactions rates are low [48]. To

identify the sources of these products of incomplete

combustion, equivalence ratio contours are pre-

sented. Figure 22 shows cut planes coincident with

the spray axis coloured by equivalence ratio and iso-

surfaces of W5 1 at several times during the com-

bustion process. Droplets are sized and coloured by

drop size and the arrows in the first image indicate

the spray trajectory.

Fig. 20 Effect of injector included angle on engineperformance and emissions. The optimizationwas conducted with an injector included angleof 155u

Fig. 21 CO distributions at 40uATDC for injectorincluded angles of 80u, 120u, and 155u



The spray targeting of the first injection of the

three cases are the bottom of the piston bowl, the

piston bowl rim, and the squish region for the 80u,120u, and 155u included angles, respectively. Target-

ing the bottom of the bowl in the first injection (i.e.

the 80u included angle case) eliminated the possibi-

lity for spray impingement on the cylinder liner.

However, since the second injection was near TDC,

the steep included angle resulted in a short fuel jet

prior to impinging on the piston bowl. The short fuel

jet reduced oxygen entrainment and fuel was

confined to a very rich region in the piston bowl,

thus explaining the very high UHC and CO emis-

sions in the bowl.

The 120u included angle case targeted the rim of

the piston bowl during the first injection. Targeting

the rim of the piston bowl confined most of the fuel

to the piston bowl, therefore reducing the possibility

of liner impingement; however, the flatter spray

angle (compared to the 80u case) increased the

distance for spray penetration of the second injec-

tion prior to impinging on the piston bowl. The

increased penetration allowed additional oxygen

entrainment, thus lowering the equivalence ratio of

the mixture in the bowl and explaining the lower

UHC and CO concentrations in the bowl.

The first injection of the 155u included angle case

targets the squish region. Since the fuel quantity is

small and the injection pressure is relatively low,

liquid impingement on the liner is not observed.

Thus, spray wall impingement is not the source of

higher UHC emissions with the 155u included angle.

The main difference between the 155u and 120u

included angle cases is the equivalence ratio near

the cylinder centerline. That is, the 120u case has a

significantly broader equivalence ratio cloud, thus it

appears that the CO and UHC reductions for the

120u included angle case (compared to the 155u case)

are due to a reduction in over-mixed (excessively

lean) regions near the centreline.

6.6 Swirl ratio sweep

Figure 23 shows the effect of swirl ratio on perfor-

mance and emissions. Note that in this section the

swirl ratio is varied without considering the effect on

the pumping work required to increase swirl levelsFig. 22 Equivalence ratio distributions on a cut planecoincident with the spray axis and iso-surfacesof W5 1 (white) for injector included angles of80u, 120u, and 155u. The black region showslocations with equivalence ratios grater than1.0

Fig. 23 Effect of swirl ratio on engine performanceand emissions. The optimization was con-ducted at a swirl ratio of 2.2



(e.g. port throttling); thus, the presented ISFC values

should be considered with caution. Beginning with a

swirl ratio of one, it can be seen that the main effect

of increasing swirl was soot reduction; however, as

the swirl ratio was increased past 5.0, soot began to

increase. Since soot emissions show the largest

sensitivity to swirl ratio, soot is the focus of the

analysis in this section.

It is of interest to understand the effect of swirl

ratio on the mixing processes that control soot

formation and oxidation. Figure 24 shows the cylin-

der pressures, heat release rates, and soot histories

for four swirl ratios – 1.0, 2.2, 4.0, and 7.0. Several

observations can be made from Fig. 24. First, the

case with a swirl ratio of 1.0 shows a significantly

advanced combustion phasing and onset of soot

formation. Next, the combustion rate for the case

with a swirl ratio of 1.0 is significantly lower than the

higher swirl ratio cases. The onset of heat release and

soot formation is identical for the 4.0 and 7.0 swirl

ratio cases, but the heat release rate slows down

earlier for the 7.0 swirl ratio case and the soot

formation process is extended.

Figure 25 shows contours of equivalence ratio for

each case near to the start of combustion. The first

two crank angles presented in Fig. 25 (i.e. 210u and

22u ATDC) show the mixture that results from

the first injection and the second two crank angles

(i.e. 4u and 6uATDC) show the spray and mixing

processes of the second injection. Note that the

limits of equivalence ratio contours for the first two

crank angles (i.e. crank angles of 210u and 22uATDC) only range from 0.3 to 0.6 owing to the small

quantity of fuel injected in the first pulse. The

equivalence ratio limits for the later crank angles

range from 0.3 to 2.0. It can be seen that the case

with a swirl ratio of 1.0 has a region of relatively high

equivalence ratio near the piston bowl rim. It

appears that both the first and second injections

interact with the piston bowl surface for the swirl

ratio 1.0 case. Increasing the swirl ratio to 2.2

reduced the spray penetration of both injections

and a smaller high equivalence ratio region near the

piston bowl rim is observed. As the swirl ratio is

increased to 4.0, the spray is more influenced by the

bulk in-cylinder swirl (shown by the apparent

‘bending’ of the second injection pulse) and the

interaction with the piston bowl and the relatively

high equivalence ratio region observed for the

SR 5 1.0 case are further reduced. Notice that at a

swirl ratio of 4.0, the first injection becomes nearly

uniformly mixed at an equivalence ratio of 0.45.

As the swirl ratio is increased to 7.0, the spray

penetration is further reduced and the fuel cloud is

confined to the centre of the combustion chamber.

Figure 26 shows contours of temperature with an

iso-surface of soot at a mass fraction of 1.061025 for

Fig. 24 Cylinder pressures, heat release rates, and soothistories for cases with swirl ratios of 1.0, 2.2,4.0, and 7.0

Fig. 25 Equivalence ratio distribution on cut planescoincident with the spray axis for swirl ratiosof 1.0, 2.2, 4.0, and 7.0



the four swirl ratio cases. At 4u ATDC, the swirl ratio

1.0 case shows combustion occurs in the high

equivalence ratio region identified in Fig. 25 and soot

has begun to form around the spray plume of the

second injection. At this time, very little heat release is

observed for the higher swirl ratio cases. By 10u ATDC,

all cases show significant heat release. The location of

combustion is seen to be a function of swirl ratio; the

high-temperature region extends furthest towards the

liner for the swirl ratio 1.0 case and is mostly confined

to the bowl for the swirl ratio 7.0 cases. The swirl ratio

2.2 and 4.0 cases show a nearly equal distribution of

high temperature in the bowl and squish regions. The

location of the soot clouds for each case has a similar

dependence on swirl ratio. It appears that the

observed low combustion rate for the swirl ratio 1.0

case is due to the confinement of combustion to the

squish region. Since the squish volume is relatively

small, oxygen is rapidly depleted and the combustion

rate then becomes controlled by a relatively small

mixing surface near the piston bowl rim. Additionally,

notice that increasing the swirl ratio from 2.2 to 4.0

appears to reduce the soot formed deep in the piston

bowl. It is thought that the reduced soot in the bowl is

due to the reduced spray-bowl interaction that results

from the reduced spray penetration with increased

swirl.

At 16u ATDC, the soot cloud and high-temperature

region for the case with a swirl ratio of 7.0 has moved

very near the cylinder centreline, while the swirl

ratio 4.0 case shows reaction occurring nearly

uniformly across the upper portion of the combus-

tion chamber. Confinement of the reaction region

to the centreline appears to limit the exposure to

oxygen and reduced soot oxidation compared to the

swirl ratio 4.0 case, thus explaining the slight

increase in soot as swirl ratio was increased from

4.0 to 7.0. The swirl ratio 1.0 case is unable effectively

to access oxygen in the centreline region and, similar

to the swirl ratio 2.2 case, shows significant soot near

the location of spray-bowl interaction. The swirl

ratio 7.0 has reduced spray penetration and very

little interaction with the piston bowl; however, the

soot cloud is confined to the centreline and this case

is unable to access oxygen in the squish region. The

swirl ratio 4.0 case is a compromise – the spray

penetration is reduced such that rich regions near

the location of spray-bowl interaction are reduced

and the fuel cloud is exposed to oxygen in the

squish, bowl, and centreline regions.

6.7 Identification of an improved solution

The parameter sweeps have shown that improve-

ments can be made to the optimization solution to

better meet the emissions and performance targets.

However, it should be noted that since each

improvement came at the expense of other objec-

tives (e.g. increased NOx accompanying reduced

ISFC), the improved solutions do not dominate the

baseline case; thus, they exist on the same Pareto

front as the baseline case. This section first sum-

marizes the results of the parameter sweeps and

then presents improved cases that give low fuel

consumption and low engine noise while meeting

emissions targets.

Reducing the first pulse injection pressure resulted

in fewer regions of high equivalence ratio, slightly

delayed the combustion phasing, and reduced the

peak PRR from 7.8 to 4.3 bar/deg, with an increase of

less than 1 per cent in fuel consumption (the

increase in fuel consumption was the result of

delayed combustion phasing). Therefore, to meet

the target PRR limit of 5.5 bar/deg, a first pulse

injection pressure of 100 bar was selected. The first

pulse injection timing sweep showed that the results

were relatively insensitive to the first pulse injection

timing owing to the small quantity of fuel injected;

thus, the optimization selected first pulse injection

timing was retained. The second pulse injection

timing sweep showed that a near TDC second pulse

injection was capable of achieving low soot and

NOx, while retaining significant control over the

phasing of the high-temperature heat release. Thus,

Fig. 26 Temperature contours on cut planes coinci-dent with the spray axis for swirl ratios of 1.0,2.2, 4.0, and 7.0. The iso-surface shows soot ata mass fraction of 1.061025



it appears that the second pulse injection timing

selected by the optimization is a good choice for

future work.

Although the soot levels of the baseline case meet

the target values presented in Table 5, the para-

metric study presented above has shown that further

soot reduction is possible. It is desirable to reduce

soot emissions further to reduce the engine after-

treatment requirements. Additionally, the selected

case did not meet the CO and UHC targets of

Table 5; thus, the results of the parametric study

were used to select parameters to drive down CO

and UHC levels. The sweep of the second pulse

injection pressure showed that a high-pressure

second injection is required to minimize soot

emissions. Additionally, the second pulse injection

pressure sweep showed that increasing the injection

pressure from the baseline value of 1500 bar to

1800 bar resulted in a slight reduction in UHC and an

improvement in fuel consumption; thus, an injec-

tion pressure of 1800 bar was selected for the

improved solution. The sweep of swirl ratio showed

that soot could be further reduced by increasing the

swirl ratio to improve mixture preparation and to

limit the spray interaction with the piston bowl. It

was found that a minimum in soot emissions existed

at a swirl ratio of 5.0; however, the reductions in soot

tended to level off near a swirl ratio of 4.0. Swirl ratio

increases generally come at the expense of increased

pumping work because of the port throttling

required to reach high swirl level. Based on these

considerations, the lowest swirl ratio that achieved

near-minimum soot levels (i.e. a swirl ratio of 4.0)

was selected for the improved solution.

The sweep of nozzle included angle showed that

significant reductions in soot, CO, and UHC could be

achieved by using a reduced injector included angle.

It was found that a minimum in soot and near

minima in UHC and CO existed at an injector

included angle of 120u. Note that the true minima in

UHC and CO were found at an injector included angle

of 100u; however, only slight increases in UHC and CO

are found when an included angle of 120u is used

rather than 100u. Based on these finding, an included

angle of 120u was selected for the improved solution.

The reductions in soot, UHC, CO, and fuel con-

sumption found by increasing the injection pressure

from 1500 to 1800 bar, reducing the included angle

from 155u to 120u, and increasing the swirl ratio from

2.2 to 4.0 all came at the expense of increased NOx and

peak PRR. However, it is widely known that EGR is

capable of controlling NOx emissions (e.g. Park and

Reitz [11]). EGR has also been shown to control

combustion phasing and PRR (e.g. Sjoberg and Dec

[9]); thus, the improved solution is presented over an

EGR sweep from which the engine designer may

choose the appropriate level of EGR to meet specific

requirements for NOx, PRR, and fuel consumption.

Table 6 shows the parameters of the improved solu-

tion and Fig. 27 shows the effect of EGR rate on this

solution.

Finally, the case with 56 per cent EGR was selected

for further investigation since it best meets the

emissions and performance targets. Since the above

simulations were conducted on a coarse mesh in

order to reduce computational expense, it is of

interest to repeat the simulations on a fine mesh to

ensure that the predictive nature of the code was not

sacrificed in the interest of computational efficiency.

The grids used for this study were given in Fig. 2. The

fine grid contains ,30 000 cells at BDC with an

average cell size of 1.2 mm. The coarse grid contains

,11 000 cells at BDC with an average cell size of

2.5 mm. The run times were 80 and 25 h for the fine

and coarse grids, respectively. Figure 28 shows the

predicted cylinder pressure, heat release rate, emis-

sions, and performance predictions on the fine and

coarse grid. The cylinder pressure, heat release rate,

CO, UHC, ISFC, and peak PRR show very little mesh

size dependency, while soot and NOx show the most

mesh dependency. It is expected that the very low

soot levels of this condition would be sensitive to

slight changes in the combustion characteristics.

Furthermore, because the combustion model con-

siders each cell as a WSR, it is likely that reducing the

grid size results in an improved prediction of local

mixture inhomogeneity that may be the source of

the observed differences in soot and NOx levels.

However, since the coarse and fine grid solutions are

reasonably close and the model validation was

conducted on a coarse grid, it is concluded that the

exhibited grid size dependency will not have an

impact on the conclusions of the present parametric

study. The results of the 56 per cent EGR case

(conducted on the fine mesh) show near-zero NOx

and soot levels, significant reductions in UHC and

Table 6 Parameters of the improved solution

Early injection pressure 100 barLate injection pressure 1800 barEarly injection timing 243 uATDCLate injection timing 21.3 uATDCIntake surge tank pressure 1.79 barTotal fuel 14.9 mg/strokeIVC timing 2104 uATDCFraction of fuel in the first pulse 0.3Nozzle included angle 120uSwirl ratio 4.0




CO compared to the optimization case, acceptable

peak PRR, and a net ISFC of ,175 g/kW h.

7 CONCLUSION

This paper provides guidelines for solving problems

with premixed combustion strategies: excessive PRR,

lack of combustion phasing control, and spray–wall

impingement owing to early injections. A multiple-

injection strategy was used to control combustion

phasing and the VPP injection system of Kokjohn et

al. [19], capable of delivering multiple pulses at

different injection pressures in the same engine

cycle, was used to reduce spray–liner impingement.

Engine experiments of Opat et al. [4] and Kokjohn

et al. [19] were used for validation of the computa-

tional model. It was found that the models were able

to reproduce the experimental pressure and heat

release traces moderately well, suggesting the pre-

dicted ISFC and peak PRR levels are reasonable.

Although, the trends were captured well, NOx levels

were slightly under-predicted at all conditions

investigated. Simulated soot and CO levels were

similar to the experimental values; however, as the

injection timing was advanced, the predicted soot

and CO trends deviated somewhat from the mea-

sured values. Thus, some caution must be exercised

when interpreting the soot and CO emissions results.

The models were able to reproduce the correct UHC

trends; however, the magnitude of UHC was under-

predicted for the single-injection model validation

and over-predicted for the split-injection model

validation. The differences in the UHC magnitudes

are thought to be the result of the simplified crevice

geometry used in this study.

The model was used to investigate the effects of

several design parameters on a premixed combus-

tion strategy found from a previous computational

optimization [19]. The effects of first and second

pulse injection pressure and timing, swirl ratio, and

nozzle included angle were presented. Each sweep

showed the effect of a single design parameter on

emissions and performance with all other para-

meters held fixed at the values given in Table 4.

Because only a single parameter was varied, the

effect of each parameter on emissions and perfor-

mance was isolated. However, the disadvantage of

this approach is that interactions between variables,

which may be important for further reducing

emissions and improving performance, are not

identified. The principal findings of this study are

summarized below.

1. Reducing the injection pressure of the first pulse

caused the spray to be more influenced by in-

cylinder flows and penetration was reduced.

Furthermore, the increased duration prevented fuel

from impinging on the piston top (a spray targeting

effect) and eliminated the resulting relatively rich

area where high-temperature heat release was

observed prior to the second injection. This resulted

in a slight delay in combustion phasing and

reduced the peak PRR from 7.8 to 4.3 bar/deg, with

less than a 1 per cent increase in fuel consumption.

The increase in fuel consumption was the result of

the delayed combustion phasing.

Fig. 27 EGR sweep of the improved solution. The starsindicate the parameters chosen by the GA andthe solid line shows the target for eachobjective




2. The injection timing sweeps showed that the use

of an optimized low-pressure early cycle injection

combined with a high-pressure near-TDC injec-

tion is capable of achieving near-zero NOx and

soot levels, while maintaining sufficient control

over the combustion phasing. It was found that as

the second pulse injection timing was advanced

beyond 15u BTDC, the injection and combustion

events become decoupled and controlling com-

bustion phasing might be a challenge.

3. Reducing the nozzle included angle from 155u to

120u resulted in significant reductions in soot, CO,

and UHC due to improved fuel distribution.

4. By increasing the swirl ratio from 2.2 to 4.0, the

soot cloud moves towards the centre of the bowl

and has increased exposure to oxygen. Further

increases in swirl ratio caused the soot cloud to be

trapped near the nozzle, where limited oxygen

was available, thus resulting in reduced soot

oxidation and increased soot emissions.

5. In general, the observed reductions in soot, UHC,

and CO came at the expense of increased NOx

and peak PRR for each of the parameter sweeps.

However, the EGR ratio sweep of the improved

solution showed that NOx and peak PRR were

easily controlled by increasing EGR. While EGR

was effective at controlling NOx and peak PRR, it

should be noted that these reductions came at the

expense of increased UHC and fuel consumption.

An improved solution was found with near-zero

NOx and soot, a net ISFC of only 175 g/kW h, and

a peak PRR of ,8 bar/deg. The solution’s operat-

ing parameters are presented in Table 6 and its

sensitivity to EGR rate is shown in Fig. 27.

ACKNOWLEDGEMENTS

Financial support from the US Department of Energy(DOE) HCCI contract # DE-FC04-02AL67612 and fromthe Engine Research Center’s Diesel Engine ResearchConsortium (DERC) member companies are gratefullyacknowledged. The authors would like to thank Thad-deus Swor and Chad Koci for their experimental data.

F Authors 2010

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https://www.researchgate.net/publication/245353569_Reduction_of_Numerical_Parameter_Dependencies_in_Diesel_Spray_Models?el=1_x_8&enrichId=rgreq-b4f618e4518dbb00708af51af91c0e99-XXX&enrichSource=Y292ZXJQYWdlOzIzODE4NjU3OTtBUzo5NzMwMjk5MTYwNTc2MkAxNDAwMjEwMjQ5MjEw










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APPENDIX

Definition of acronyms

AIS adaptive injection strategies

AMC adaptive multi-grid chemistry

ATDC after top dead centre

BSFC brake specific fuel consumption

BTDC before top dead centre

CA crank angle

CFD computational fluid dynamics

CO carbon monoxide

COSSO component selection smoothing

operator

DERC Diesel Emissions Reduction

Consortium

EGR exhaust gas recirculation

EOI end of injection

EPA Environmental Protection Agency

ERC Engine Research Center

FID flame ionization detector

FTIR Fourier transform infrared

spectroscopy

GA genetic algorithm

GRI Gas Research Institute

HCCI homogeneous charge compression

ignition

HPCR high-pressure common rail

IMEP indicated mean effective pressure

ISFC indicated specific fuel consumption

IVC intake valve closure

KH Kelvin–Helmholtz

LDEF Lagrangian drop–Eulerian fluid

LPS low-pressure system

MOGA multi-objective genetic algorithm

NOx nitric oxides

PAH polycyclic aromatic hydrocarbons

PLIF planar laser-induced fluorescence

PCCI premixed charge compression

ignition

PRR pressure rise rate



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RT Rayleigh–Taylor

SOI start of injection

TDC top dead centre

TSC two-stage combustion

UHC unburned hydrocarbon

UNIBUS uniform bulky combustion system

VGS variable geometry sprays

VPP variable pressure pulse

VVA variable valve actuation

WSR well-stirred reactor



Investigation of charge preparation strategies for controlled premixed charge compression ignition combustion using a variable pressure injection system

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